Check

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import argparse
import json
import sys

import argparse import json import sys

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import numpy as np
import pandas as pd
from torch.utils.data import DataLoader, Dataset
from tqdm import tqdm
import torch
from torch import nn
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
print(device)

import numpy as np import pandas as pd from torch.utils.data import DataLoader, Dataset from tqdm import tqdm import torch from torch import nn device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") print(device)

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from model1.single import single
from model1.fusion import fusion
from model1.fusionWithCrossAttention import fusionWithCrossAttention
from func import int2cat, set_seed

from model1.single import single from model1.fusion import fusion from model1.fusionWithCrossAttention import fusionWithCrossAttention from func import int2cat, set_seed

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class MyDataset3(Dataset):
    def __init__(self, vfeats, tfeats):
        self.vfeats = vfeats
        self.tfeats = tfeats

    def __len__(self):
        return len(self.vfeats)

    def __getitem__(self, idx):
        vfeat = self.vfeats[idx]
        tfeat = self.tfeats[idx]
        return torch.FloatTensor(vfeat), torch.FloatTensor(tfeat)

class MyDataset3(Dataset): def __init__(self, vfeats, tfeats): self.vfeats = vfeats self.tfeats = tfeats def __len__(self): return len(self.vfeats) def __getitem__(self, idx): vfeat = self.vfeats[idx] tfeat = self.tfeats[idx] return torch.FloatTensor(vfeat), torch.FloatTensor(tfeat)

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def predict(hyper_dict, test_loader):
    print(hyper_dict)
    print("using {} device.".format(device))
    # 直接加载
    net = torch.load(hyper_dict.model_loc)
    net.eval()
    all_preds = []

    with torch.no_grad():
        val_bar = tqdm(test_loader, file=sys.stdout)

        for val_data in val_bar:
            val_images, val_text = val_data
            outputs = net(val_images.to(device), val_text.to(device))
            preds = torch.argmax(outputs.data, 1)
            all_preds.extend(preds.cpu().numpy().flatten())

    return all_preds

def predict(hyper_dict, test_loader): print(hyper_dict) print("using {} device.".format(device)) # 直接加载 net = torch.load(hyper_dict.model_loc) net.eval() all_preds = [] with torch.no_grad(): val_bar = tqdm(test_loader, file=sys.stdout) for val_data in val_bar: val_images, val_text = val_data outputs = net(val_images.to(device), val_text.to(device)) preds = torch.argmax(outputs.data, 1) all_preds.extend(preds.cpu().numpy().flatten()) return all_preds

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if __name__ == '__main__':
    parser = argparse.ArgumentParser(description="check")
    parser.add_argument('--vtype', type=str, default='clip', help='resnet or clip')
    parser.add_argument('--ttype', type=str, default='clip', help='roberta or clip')
    parser.add_argument('--batchsize', type=int, default=1024, help='128 512 1024')
    parser.add_argument('--model_loc', type=str, default='saved/saved_models/clip.pth',help='saved models\' location')
    args = parser.parse_args()
    # 读取数据集与特征信息
    feats_text = json.load(open('saved/%s_train.json' % args.ttype, 'r'))
    feats_img = json.load(open('saved/%s_train.json' % args.vtype, 'r'))

    feats_text = feats_text['txt_feats']
    feats_img = feats_img['img_feats']

    feats_text = np.array(feats_text)
    feats_img = np.array(feats_img)
    print(len(feats_text), len(feats_img))

    # 构建数据集与dataloader
    test_data = MyDataset3(feats_img, feats_text)
    test_loader = DataLoader(dataset=test_data, batch_size=args.batchsize)

    # 推理
    res = predict(args, test_loader)
    df = pd.read_csv("data/train.txt")
    df["pred"] = res
    df["pred"] = df["pred"].map(int2cat)
    df["diff"] = df["tag"] != df["pred"]
    df.to_csv("output/tmp_no_open_check.txt", index=False)

    neg2pos = 0
    pos2neg = 0
    neu2pos = 0
    neu2neg = 0
    pos2neu = 0
    neg2neu = 0
    cnt = 0

    for _, i in df.iterrows():
        if i["pred"] == "negative" and i["tag"] == "positive":
            pos2neg += 1
            cnt += 1
        elif i["pred"] == "positive" and i["tag"] == "negative":
            neg2pos += 1
            cnt += 1
        elif i["pred"] == "neutral" and i["tag"] == "positive":
            pos2neu += 1
            cnt += 1
        elif i["pred"] == "neutral" and i["tag"] == "negative":
            neg2neu += 1
            cnt += 1
        elif i["pred"] == "positive" and i["tag"] == "neutral":
            neu2pos += 1
            cnt += 1
        elif i["pred"] == "negative" and i["tag"] == "neutral":
            neu2neg += 1
            cnt += 1

    print(neg2pos, pos2neg, neu2pos, neu2neg, pos2neu, neg2neu, 1- cnt / len(feats_text))

if __name__ == '__main__': parser = argparse.ArgumentParser(description="check") parser.add_argument('--vtype', type=str, default='clip', help='resnet or clip') parser.add_argument('--ttype', type=str, default='clip', help='roberta or clip') parser.add_argument('--batchsize', type=int, default=1024, help='128 512 1024') parser.add_argument('--model_loc', type=str, default='saved/saved_models/clip.pth',help='saved models\' location') args = parser.parse_args() # 读取数据集与特征信息 feats_text = json.load(open('saved/%s_train.json' % args.ttype, 'r')) feats_img = json.load(open('saved/%s_train.json' % args.vtype, 'r')) feats_text = feats_text['txt_feats'] feats_img = feats_img['img_feats'] feats_text = np.array(feats_text) feats_img = np.array(feats_img) print(len(feats_text), len(feats_img)) # 构建数据集与dataloader test_data = MyDataset3(feats_img, feats_text) test_loader = DataLoader(dataset=test_data, batch_size=args.batchsize) # 推理 res = predict(args, test_loader) df = pd.read_csv("data/train.txt") df["pred"] = res df["pred"] = df["pred"].map(int2cat) df["diff"] = df["tag"] != df["pred"] df.to_csv("output/tmp_no_open_check.txt", index=False) neg2pos = 0 pos2neg = 0 neu2pos = 0 neu2neg = 0 pos2neu = 0 neg2neu = 0 cnt = 0 for _, i in df.iterrows(): if i["pred"] == "negative" and i["tag"] == "positive": pos2neg += 1 cnt += 1 elif i["pred"] == "positive" and i["tag"] == "negative": neg2pos += 1 cnt += 1 elif i["pred"] == "neutral" and i["tag"] == "positive": pos2neu += 1 cnt += 1 elif i["pred"] == "neutral" and i["tag"] == "negative": neg2neu += 1 cnt += 1 elif i["pred"] == "positive" and i["tag"] == "neutral": neu2pos += 1 cnt += 1 elif i["pred"] == "negative" and i["tag"] == "neutral": neu2neg += 1 cnt += 1 print(neg2pos, pos2neg, neu2pos, neu2neg, pos2neu, neg2neu, 1- cnt / len(feats_text))